Cigarette Burning Quality Indicator Evaluation Using Isolation Forest
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Solution Overview
Problem
The tobacco industry faces challenges in objectively evaluating the importance of various cigarette burning quality indicators, as existing methods lack comprehensive analysis and correlation assessment, leading to inconsistent quality evaluations.
Innovation Solution
A method involving data mining techniques, specifically using an isolation forest algorithm to detect outliers and combine grayscale correlation, Euclidean distance, city block distance, and cosine similarity analyses to comprehensively evaluate and rank the importance of cigarette burning quality indicators, collected through simulated smoking processes using robotic arms and high-speed cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple cigarette burning quality indicators are measured, then the comprehensiveness of quality evaluation is improved, but the complexity of analysis and evaluation increases
Solution Approach 1:
The patent segments the complex evaluation process into distinct phases: data collection from multiple indicators, outlier detection using isolation forest, and correlation analysis using multiple distance metrics. This segmentation makes the overall complex analysis manageable by breaking it into smaller, systematic steps.
Solution Approach 2:
The patent introduces intermediary computational tools (isolation forest algorithm, grayscale correlation, Euclidean distance, city block distance, cosine distance) that mediate between the raw multi-indicator data and the final quality evaluation, transforming complex data relationships into interpretable results.
2Ease of operation
If traditional visual evaluation methods are used, then the simplicity of evaluation is maintained, but the objectivity and precision of quality assessment deteriorates
Solution Approach 1:
The patent replaces traditional manual visual evaluation with automated robotic systems that use cameras and data algorithms to collect and analyze cigarette burning indicators, substituting human subjective judgment with objective mechanical measurement and computational analysis.
Solution Approach 2:
The patent uses high-speed cameras to create visual copies and records of cigarette burning processes, allowing repeated objective analysis of the same burning events without human intervention, thereby maintaining measurement precision while simplifying the evaluation operation.
3Reliability
If outlier samples are included in analysis, then the representativeness of data distribution is improved, but the stability of evaluation results deteriorates
Solution Approach 1:
The patent extracts and identifies outlier samples from the dataset using the isolation forest algorithm, separating them from the main data distribution. This allows the evaluation to focus on representative samples while still understanding the presence of outliers, thereby stabilizing evaluation results without completely discarding extreme cases.
Solution Approach 2:
The patent applies different analytical treatments to different portions of the data: representative samples undergo comprehensive correlation analysis while outlier samples are identified and handled separately. This local differentiation maintains overall evaluation stability while preserving data distribution representativeness.
4Measurement precision
If comprehensive correlation analysis of multiple indicators is performed, then the accuracy of indicator importance ranking is improved, but the computational complexity increases
Solution Approach 1:
The patent merges multiple correlation analysis methods (grayscale correlation, Euclidean distance, city block distance, cosine distance) into a unified evaluation framework. By combining these different analytical approaches, the system achieves more accurate indicator importance ranking while managing computational complexity through integrated processing.
Data Source
AI summary
A method for comprehensively analyzing and/or evaluating cigarette burning quality index is disclosed. The steps include collecting cigarette burning quality index data, filtering the cigarette burning quality index data, standardizing cigarette data, and measuring cigarette burning quality. The importance of the cigarette burning quality indicators can also be evaluated. The method for comprehensively analyzing and/or evaluating cigarette burning quality indicators can reflect general laws more accurately by maintaining the sample distribution through a singularity detection method, and analyzing correlations of each index with cigarette performance from multiple perspectives, to fuse them into a comprehensive measurement value. The importance ranking and weight of indicators can be obtained more completely and stably.